Using machine learning to predict antibody response to SARS-CoV-2 vaccination in solid organ transplant recipients: the multicentre ORCHESTRA cohort

Objectives: Study aim is to assess predictors of negative antibody response (AbR) in solid organ transplant (SOT) recipients after the first booster of SARS-CoV-2 vaccination. Methods: SOT recipients receiving SARS-CoV-2 vaccination were prospectively enrolled (March 2021-January 2022) at six hospitals in Italy and Spain. AbR was assessed at first dose (t0), second dose (t1), 3±1 month (t2), and 1 month after third dose (t3). Negative AbR at t3 was defined as anti-receptor binding domain titre <45 BAU/mL. Machine Learning models were developed to predict the individual risk of negative (vs. positive) AbR using as covariates age, type of transplant, time between transplant and vaccination, immunosuppressive drugs, type of vaccine, and graft function, and subsequently assessed using a validation cohort. Results: Overall, 1615 SOT recipients (1072 [66.3%] males, mean±standard deviation (SD) age 57.85±13.77) were enrolled and 1211 received three vaccination doses. Negative AbR rate decreased from (886/946) 93.66% to (202/923) 21.90% from t0 to t3. Univariate analysis showed that older patients (mean age 60.21±11.51 vs. 58.11±13.08), anti-metabolites (57.9% vs. 35.1%) steroids (52.9% vs. 38.5%), recent transplantation (<3 years) (17.8% vs. 2.3%), and kidney, heart, or lung compared to liver transplantation (25%, 31.8%, 30.4% vs. 5.5%) had a higher likelihood of negative AbR. Machine learning algorithms showing best prediction performance were logistic regression (precision recall curve-PRAUC mean 0.37 [95%CI 0.36-0.39]) and k-Nearest Neighbors (PRAUC 0.36 [0.35-0.37]). Conclusions: Almost a quarter of SOT recipients showed negative AbR after first booster dosage. Unfortunately, clinical information cannot efficiently predict negative AbR even with ML algorithms.